XCycles Backprojection Acoustic Super-Resolution

Sensors

An Braeken¹  Bruno da Silva¹  Kris Steenhaut¹  Jurgen Vandendriessche¹  Feras Almasri¹  Abdellah Touhafi¹  Laurent Segers¹  Olivier Debeir² 

[1] Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel, 1050 Brussels, Belgium;Laboratory of Image Synthesis and Analysis (LISA), Université Libre de Bruxelles, 1050 Brussels, Belgium;

关键词: super-resolution;    acoustic imaging;    acoustic camera;    delay-and-sum beamformer;   

DOI  :  10.3390/s21103453

来源: DOAJ

【 摘 要 】

The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they allow people to visualize the intensity of sound waves beyond the visible spectrum. However, because of the limitations imposed on acquiring acoustic data, new methods for improving the resolution of the acoustic images are necessary. At this time, there is no acoustic imaging dataset designed for the SR problem. This work proposed a novel backprojection model architecture for the acoustic image super-resolution problem, together with Acoustic Map Imaging VUB-ULB Dataset (AMIVU). The dataset provides large simulated and real captured images at different resolutions. The proposed XCycles BackProjection model (XCBP), in contrast to the feedforward model approach, fully uses the iterative correction procedure in each cycle to reconstruct the residual error correction for the encoded features in both low- and high-resolution space. The proposed approach was evaluated on the dataset and showed high outperformance compared to the classical interpolation operators and to the recent feedforward state-of-the-art models. It also contributed to a drastically reduced sub-sampling error produced during the data acquisition.

【 授权许可】

Unknown